{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Ridge Regression"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ridge Regression is a technique for analyzing multiple regression that suffer from multicollinearity. However, multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value.\n",
        "\n",
        "\n",
        "Regularization is for solving over-fitting problem which implies model performing well on training data. However, it performing poorly on validation test data. In addition, regularization solves problem by adding a penatly term to the objective function and control the model complexity using that penalty term.\n",
        "\n",
        "1. Large number of variables\n",
        "2. Low ratio of number observations to number of variables\n",
        "3. High Multi-Collinearity\n",
        "\n",
        "\n",
        "There are 2 Loss function in Regularization.  \n",
        "\n",
        "L1 Loss function or L1 Regularization is minimizing the objective function by adding a penalty term to the sum of the absolute values of coefficients. This is called least absolute deviations method.\n",
        "\n",
        "L2 Loss function or L2 Regularization is to minimize the objective function by adding a penalty term to the sum of the squares of cofficients. "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.85</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Open  High   Low  Close  Adj Close    Volume\n",
              "Date                                                    \n",
              "2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n",
              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n",
              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n",
              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n",
              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.012658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.032500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.012107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-09</th>\n",
              "      <td>4.20</td>\n",
              "      <td>4.23</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.09</td>\n",
              "      <td>30667600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.021531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Open  High   Low  Close  Adj Close    Volume  Increase_Decrease  \\\n",
              "Date                                                                          \n",
              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200                  1   \n",
              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n",
              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n",
              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n",
              "2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n",
              "\n",
              "            Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "Date                                              \n",
              "2014-01-03                 1         1  0.012658  \n",
              "2014-01-06                 1         1  0.032500  \n",
              "2014-01-07                 1         0  0.012107  \n",
              "2014-01-08                 0         0  0.000000  \n",
              "2014-01-09                 0         1 -0.021531  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.drop(['Adj Close', 'Close'], axis=1)\n",
        "y = dataset['Adj Close'].values.reshape(-1,1) "
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=3)\n",
        "print(len(X_test), len(y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "352 352\n"
          ]
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rr = Ridge(alpha=0.01) # higher the alpha value, more restriction on the coefficients; low alpha > more generalization, coefficients are barely\n",
        "# restricted and in this case linear and ridge regression resembles\n",
        "# Underfitting -  decrease alpha\n",
        "# Overfitting - increase alpha\n",
        "rr.fit(X_train, y_train)\n",
        "\n",
        "rr100 = Ridge(alpha=100) #  comparison with alpha value\n",
        "rr100.fit(X_train, y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": [
              "Ridge(alpha=100, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "   normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rr.score(X_train, y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": [
              "0.99962789000927199"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Ridge_train_score = rr.score(X_train,y_train)\n",
        "Ridge_test_score = rr.score(X_test, y_test)\n",
        "\n",
        "Ridge_train_score100 = rr100.score(X_train,y_train)\n",
        "Ridge_test_score100 = rr100.score(X_test, y_test)"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"ridge regression train score low alpha:\", Ridge_train_score)\n",
        "print(\"ridge regression test score low alpha:\", Ridge_test_score)\n",
        "print(\"ridge regression train score high alpha:\", Ridge_train_score100)\n",
        "print(\"ridge regression test score high alpha:\", Ridge_test_score100)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ridge regression train score low alpha: 0.999627890009\n",
            "ridge regression test score low alpha: 0.999638170233\n",
            "ridge regression train score high alpha: 0.99927759257\n",
            "ridge regression test score high alpha: 0.999293501107\n"
          ]
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(rr.coef_,alpha=0.7,linestyle='none',marker='*',markersize=5,color='red',label=r'Ridge; $\\alpha = 0.01$',zorder=7) # zorder for ordering the markers\n",
        "\n",
        "plt.plot(rr100.coef_,alpha=0.5,linestyle='none',marker='d',markersize=6,color='blue',label=r'Ridge; $\\alpha = 100$') # alpha here is for transparency\n",
        "\n",
        "plt.xlabel('Coefficient Index',fontsize=16)\n",
        "plt.ylabel('Coefficient Magnitude',fontsize=16)\n",
        "plt.legend(fontsize=13,loc=4)\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Coefficients:', rr.coef_)\n",
        "print('Intercept:', rr.coef_)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficients: [[ -2.62325641e-01   6.40217463e-01   6.24258888e-01  -6.85847006e-10\n",
            "    3.19331279e-03   3.85846750e-02  -9.90763474e-04   1.07885538e+00]]\n",
            "Intercept: [[ -2.62325641e-01   6.40217463e-01   6.24258888e-01  -6.85847006e-10\n",
            "    3.19331279e-03   3.85846750e-02  -9.90763474e-04   1.07885538e+00]]\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rr.score(X_test, y_test)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": [
              "0.99963817023330459"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "version": "3.5.5",
      "file_extension": ".py",
      "nbconvert_exporter": "python",
      "name": "python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.15.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}